Ethics in the use of Data & AI

HSBC - DNA Summit - Ethics in Use of Data and AI - Speech - Kalilur Rahman v2.pptx
1

• What is Ethics?
• Benefits of AI
• Impact of AI on Ethics
• Data Canvas & Data
Privacy
• Conclusion
Agenda

Let’s start with a video of the Future of Work!
Source: Growth Tribe https://www.youtube.com/watch?v=Y9FOyoS3Fag

Perhaps the greatest Computer
Scientist ever predicting on
Machine Intelligence
We have clearly passed the
TURING TEST
• In 2014 - Eugene Goostman, Simulating a
13 year old passed the Turing test at
University of Reading
We are seeing Leaps and Bounds
in advances of Technology – in
Artificial Intelligence, Big Data,
Quantum Computing and
Processing Power!
Let’s hear!

What is Ethics
anyways?
How do you ensure the ethics of Data Capture, Maintenance and
Usage of DATA that gets doubled every two years!

8
Current Understanding of AI , Data Science , Data
Privacy or Data Ethics - A Situation of
IT was six men of Indostan
To learning much inclined,
Who went to see the Elephant
(Though all of them were blind),
That each by observation
Might satisfy his mind.
And so these men of Indostan
Disputed loud and long,
Each in his own opinion
Exceeding stiff and strong,
Though each was partly in the
right,
And all were in the wrong!
From - The Blind Men and the
Elephant by John Godfrey Saxe

HSBC - DNA Summit - Ethics in Use of Data and AI - Speech - Kalilur Rahman v2.pptxSource : https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
A Good Explanation of Progress of AI
E a r l y A I
Basic Turing Test Style
Use of Memory and Knowledge
Post John McCarthy’s Conceptualization
Basic Robotics and Degrees of Freedom
D e e p L e a r n i n g
Rapid Infrastructure Growth
Advanced Algorithms
Big Data Explosion
Quantum Computing
M a c h i n e L e a r n i n g
Algorithms Centric
Statistics Driven
Supervised and Unsupervised Learning

https://youtu.be/zatL4uFRpC0
Fast Learning – Download and Fly an helicopter
Can AI Take us to this stage?
How about Fast Data Download – Hey – Can I test this brand new
“thing” in 2 minutes? How do we ensure Privacy and Data Ethics

Top Human World Champions Royally defeated by AI!
2011
2016
1996/97
IBM’s
Deep Blue
IBM
Watson
Google
Deepmind
AlphaGo won 60–0 rounds on two public Go websites
including 3 wins against World Go champion Ke Jie.

https://oxism.com/thing-translator/
Take it to next level
How can you easily integrate globally. Language
will no longer be a barrier.

13
A new priest named Mindar is holding
forth at Kodaiji, a 400-year-old Buddhist
temple in Kyoto, Japan.
Like other clergy members, this priest
can deliver sermons and move around
to interface with worshippers.
Mindar is a robot.
Designed to look like Kannon, the
Buddhist deity of mercy, the $1 million
machine is an attempt to reignite
people’s passion for their faith in a
country where affiliation is on the
decline. https://youtu.be/mJDf1GXaF-w
Somewhere CLOSE In INDIA
• https://youtu.be/tXvMpZHGQGA -
Have your Ganpati Arti done by a
Robot
AI for Spirituality
Source: VOX & Multiple Internet Searches

But... AI is not without controversies though!
Facebook Researchers shut down an AI
engine at the Facebook AI Research Lab
(FAIR), discovering that the AI created
its own unique language undecipherable
by humans - Simultaneous glimpse of
both the awesome and horrifying
potential of AI
Elon Musk - “AI is Potentially more
Dangerous than Nukes”
sets up a $1 billion (£770M) OpenAI.org to try and
promote safe development of AI
Vladimir Putin -
“Whoever masters AI will rule the world!”
ISAAC ASIMOV’s Laws of Robotics
1. A robot may not injure a human being or, through
inaction, allow a human being to come to harm.
2. A robot must obey orders given it by human beings
except where such orders would conflict with the
First Law.
3. A robot must protect its own existence as long as
such protection does not conflict with the First or
Second Law.

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Positive Example
Money Ball – Great example and Analogy that can be
looked at.
The best-known Moneyball theory was that on-base
percentage was an undervalued asset and sluggers were
overvalued.
Oakland Athletics and Boston Redsox used the
SaberMetrics based analysis to gain a lot of brilliant
outcomes
Not so Positive
People of African American background fill-up 40% of US
prisons – Economic impact of $31000 per year. What
about having a better systems in place to avoid this
unnecessary costs to the society?
Source: Multiple Internet Searches

17
http://moralmachine.mit.edu/results/465794528
http://moralmachine.mit.edu/

18
Microsoft’s AI Fiasco – “REPEAT AFTER ME” - TayTweets
Source: Multiple Internet Image Searches

Uber Self-Driving Car Fatality in Arizona

20
Google’s Translation – Learning Problem – And Correction
After initial launch with lesser data set, issues were
seen. After a backlash, and a bigger datasets Google
Neural Machine Translation got this right!
Train the model well – with good and diverse set of
data
Source: Multiple Internet Searches & Google Blog

21
• IBM Watson Led all the way against Jeopardy! Superstar
winners.
• Watson completely guess wrong, and had an answer so off
the mark it shocked the audience
• In response to “Its largest airport is named for a World War II
hero; its second largest, for a World War II battle” in
the category of “U.S. Cities,” Watson said “What is Toronto?”
• Reason – There is a Toronto in Illinois - Although it’s too
small to have an airport – and Toronto in Canada has an
American League baseball team. Toronto is also close the
right answer – Chicago’s O’Hare International Airport
• Watson Bet cleverly to win the game anyways!
• How would a Quizzer with a high IQ answered this question?
IBM WATSON’s Big Jeopardy Fiasco!

22
Broward County in Florida used a Software to predict and risk rank the citizens to
predict future criminals. And it’s biased against blacks.
AI Discrimination / Bias due to incorrect data
Northpointe’s core product is a
set of scores derived from 137
questions that are either
answered by defendants or pulled
from criminal records
Source: ProPublica Article

23
What are some challenges of Automated Decision Making?
Source: Future of Privacy Forum

24
Every breath you take
Every move you make
Every bond you break
Every step you take
I'll be watching you
Every single day
Every word you say
Every game you play
Every night you stay
Oh can't you see
You belong to me
My poor heart aches
With every step you take
Every move you make
Every vow you break
Every smile you fake
Every claim you stake
Since you've gone I been lost without a trace
I dream at night I can only see your face
I look around but it's you I can't replace
I feel so cold and I long for your embrace
I keep crying baby, baby, please
Oh can't you see
You belong to me
My poor heart aches
With every step you take
Every move you make
Every vow you break
Every smile you fake
Every claim you stake
Every move you make
Every step you take
- Lyrics from POLICE Song
Big Brother Policing – Ethics?
Source: Multiple Internet Searches

25
Is AI – Good or Bad? – Some Chat Examples

26
AI Driven Chatbot for Moral Policing and Spiritual
Help

27
AI Chatbot Failures – Due to Missing Context
Still a long way to go to meet
the forecast of 1-human brain
by an AI driven machine

28
More Chatbot Situations

29
What if AI is Biased?
Facebook picks 6 projects from India for ethics in AI research awards
“Mitigating Bias in Face Recognition for Vast Regional Diversity in India”
“Patient-Centric Frameworks for the Evaluation of AI-Enabled Medical Tests”
“Targeted Bias in Indian Media Outlets”
“Regulatory Impact Assessment of the National AI Market Place of India”
“A ‘Public Law of Information’ for India”
“Ethical Implications of Delegating Decision-making Journey to AI Systems”

30
Problem of Deep Fakes & Cheap Fakes
Source: Data & Society

31
Bias in Datasets
Datasets can become unintentionally biased through
• a lack of cohort diversity
• technical processes of data collection and cleaning
• the specific incorporation of electronic health record data.
Bias in Outcomes
The outcomes of precision medicine research can be discriminatory in many ways
• too much focus on individual responsibility for health
• the marginalization of those population groups with lower health literacy or in less resourced areas
• the potential to shift the accepted forms of biomedical research.
Problem of Bias in Precision Medicine
Source : Data&Society – Fairness in Precision Medicine

32
• Google isworking with the United States
Department of Defense on drone
software called "Project Maven."
• The project applied Google's image-
recognition techniques to the millions of
hours of drone footage collected by the
military with the goal of identifying
people and objects of interest.
• Laura Nolan, who resigned from Google
last year in protest at being sent to work
on a project to dramatically enhance US
military drone technology, has called for
all AI killing machines not operated by
humans to be banned.
• Nolan said killer robots not guided by
human remote control should be
outlawed by the same type of
international treaty that bans chemical
weapons.
Source: https://www.theguardian.com/technology/2019/sep/15/ex-google-worker-fears-killer-robots-cause-mass-atrocities

Drone based autonomous Weapons –Impact on humanity

34
• https://www.theguardian.com/us-
news/2019/sep/07/pentagon-military-
artificial-intelligence-ethicist
• Wanted: military “ethicist”. Skills:
data crunching, machine learning,
killer robots. Must have: cool head,
moral compass and the will to say
no to generals, scientists and even
presidents.
• The Pentagon is looking for the
right person to help it navigate the
morally murky waters of artificial
intelligence (AI), billed as the
battlefield of the 21st century.
• “One of the positions we are going to
fill will be someone who’s not just
looking at technical standards, but
who’s an ethicist,” Lt Gen Jack
Shanahan, director of the Joint
Artificial Intelligence Center (JAIC) at
the US defense department, told
reporters last week.

HSBC - DNA Summit - Ethics in Use of Data and AI - Speech - Kalilur Rahman v2.pptx 35
Can AI and Big Data help Bangalore solve the
woeful traffic Problem – and possibly become
a role-model For other big metros’ to
emulate?
Source: Inshorts / Deccan Herald

A human doctor in the US
• Goes through 16 years of education before entering medical school
• Has to pass through internship and residency before practicing medicine.
• After all these a human Dr. needs several decades of experience to become a qualified
cancer expert.
AI expert or an IBM Watson will probably need two to five years of
“learning” to become as knowledgeable.
It is the complexity of the domain that defines how long Watson will take
to learn it.
Will we have a Dr. AI How-Sir M.D?

Will we have a Dr. AI How-Sir M.D?

38
Image Source: Machine Learning, XKCD

“
39
Source: https://www.credit-suisse.com/eg/en/articles/asset-management/in-cloud-we-trust-uk-201905.tag*article-topic--asset-management---security.html
Doubling Data – Will it be a Trouble?

“
More than 5 Million
ROOMBA iROBOTs Sold in
2017
Who’s Problem is it anyways?
Will the machines be intelligent
enough to avoid issues?
Source: https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3

6 © 2019 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates.
DPO: And make sure we
protect our assets’ privacy.
CEO CISODPO
CDO CIO
Responsibility and Data Ownership Is Split
CFO: Let’s manage data as
a real asset. Data is one of
our most risky assets.
CISO: I don’t have enough
resources to protect this
toxic asset.
CMO CFO
CDO: Data is our greatest
monetization asset.
CEO: Data is one of our
greatest competitive assets.
Consultant: Data vs.
information vs.
knowledge vs.
wisdom … blah blah
blah …
Source: Gartner

HSBC - DNA Summit - Ethics in Use of Data and AI - Speech - Kalilur Rahman v2.pptx 42
How can we Govern Data ?
Source: Medium

Techniques of SOURCE HACKING provide journalists, news
organizations, platform companies, and others with a new
vocabulary
Source Hacking is a tool for generating Virality of Posts, so that
terms such as “trolling” and “trending” ado not stand in for
concerted efforts to pollute the information environment
Misinformation and
Spread of Hate /
Fake News
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Viral Sloganeering
• repackaging reactionary talking points for social media and press amplification
Leak Forgery
• prompting a media spectacle by sharing forged documents
Evidence Collages
• compiling information from multiple sources into a single, shareable document, usually as an
image
Keyword Squatting
• the strategic domination of keywords and sockpuppet accounts (A sockpuppet is an online
identity used for purposes of deception) to misrepresent groups or individuals

Data Ethics & AI –
Media Content
Moderation Case
44

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Have a Data Ethics Governance Framework
Data sources
Limitations in data sources
Sharing data with others
Ethical and legislative context
Rights around data sources
Your reason for using data
Communicating your purpose
Positive effects on people
Negative effects on people
Minimising negative impact
Engaging with people
Openness and transparency
Ongoing implementation
Reviews and iterations
Your actions
Source: The ODI

46
Use the Data Governance to drive innovation ethically
Use the data with an aim to
make a positive impact
Decide
•Your approach
Create
•Strategize and Create your Data
Governance Strategy
Steward
•Steward and maintain the data Ethically
Use
•Innovation / Capability / Infrastructure
•Ethics / Equity / Engagement
•Leverage Trust / Openness / Network
with Standardization bodies such as
theODI.org / Fast.AI etc,
Source: The ODI

47
AI Governance Model – A Layered View*
Source: Urs Gasser and Virgilio A.F. Almeida https://dash.harvard.edu/bitstream/handle/1/34390353/w6gov-18-LATEX.pdf?sequence=1
AI-based systems are “black boxes,” resulting in massive information asymmetries between the
developers of such systems and consumers and policymakers. How do we address this?
How do we handle issues such
as ?
• Justice and equality
• Use of force
• Safety and certification
• Privacy
• Displacement of labor and
taxation
• Information asymmetries
• Finding normative
consensus
• Government mismatches
AI Blackbox issues

48
AI and Governance
AI Governance Challenge in Society
• AI is built by humans and it will be shaped by human values. If we build AI systems that are a mirror to our
current societies, they will be riddled with the historical biases and inequalities of our societies - Amnesty
International
• Most of the AI programmers are White, Male and Young Computer Scientists – How will it address the
diverse landscape?
City Centric AI Policy
• In 2017, New York City passed a law that aims to help ensure that algorithms used by city agencies are
transparent, fair, and valid by setting up a task force to make recommendations on algorithmic regulation,
transparency, and bias. While these rules apply only to New York and do not appeal to human rights
directly, this move to regulate AI may become a model for other cities.
National AI Policy
• In 2018, the Australia Human Rights Commission launched a project to directly address the human rights
impact of AI and emerging technologies, which includes a robust engagement of international human
rights law and may serve as a guide for other countries

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Sample Corporate AI Principle / Vision - Google
Be socially beneficial
Avoid creating or reinforcing unfair bias
Be built and tested for safety
Be accountable to people
Incorporate privacy design principles
Uphold high standards of scientific
excellence
Be made available for uses that accord with
these principles
Technologies that cause or are likely
to cause overall harm. Where there
is a material risk of harm, we will
proceed only where we believe that
the benefits substantially outweigh
the risks, and will incorporate
appropriate safety constraints.
Weapons or other technologies
whose principal purpose or
implementation is to cause or
directly facilitate injury to people.
Technologies that gather or use
information for surveillance violating
internationally accepted norms.
Technologies whose purpose
contravenes widely accepted
principles of international law and
human rights.
Don’ts

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